This course focuses on developing Python skills for assembling business data. It will cover some of the same material from Introduction to Accounting Data Analytics and Visualization, but in a more general purpose programming environment (Jupyter Notebook for Python), rather than in Excel and the Visual Basic Editor. These concepts are taught within the context of one or more accounting data domains (e.g., financial statement data from EDGAR, stock data, loan data, point-of-sale data).
The first half of the course picks up where Introduction to Accounting Data Analytics and Visualization left off: using in an integrated development environment to automate data analytic tasks. We discuss how to manage code and share results within Jupyter Notebook, a popular development environment for data analytic software like Python and R. We then review some fundamental programming skills, such as mathematical operators, functions, conditional statements and loops using Python software.
The second half of the course focuses on assembling data for machine learning purposes. We introduce students to Pandas dataframes and Numpy for structuring and manipulating data. We then analyze the data using visualizations and linear regression. Finally, we explain how to use Python for interacting with SQL data.

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INTRODUCTION TO THE COURSE

In this module, you will become familiar with the course, your instructor and your classmates, and our learning environment. This orientation module will also help you obtain the technical skills required to navigate and be successful in this course.

講師

Ronald Guymon

Linden Lu

字幕

Hi, my name is Linden Lu. I grew up in Beijing, China. After getting my Bachelors in Computer Science from Tsinghua University, I moved to the States in 1997 and got my Masters in Computer Science from Indiana University. I then worked as a software engineer and consultant for over ten years in various Industries, including publication, media, e-commerce, finance, and insurance, mainly in California. I moved to Champaign with my family in 2006 and later got Masters in Finance from University of Illinois. I joined the Gies College of Business in 2017 and have been teaching accounting students data analytics since them. This is truly my dream job because it combined my expertise in programming and data analytics with a passion in teaching. In my spare time, I enjoy all kinds of sports, among them golf is my favorite. I'm going to show you my home the University of Illinois golf course today. Here we are at the University of Illinois golf course. There are actually two 18-hole courses in this facility, orange on this side and blue on the other side. The first hole on the orange course is the 540 yard, par 5. On a good day with the help of tailwind, I can reach the green in two shots. Let's see what I can do today. I hit the ball into the loose on the left. Let's hope I can find it. Okay, here's my ball surrounded by trees. I have two options here. I can punch the ball side way onto the fairway. With this approach, I'm not likely to get a birdie, but I have a good chance to save a par. Another option is there's a small opening to the green from here. If I can hit the ball through the gap between the trees and get close to the green, I have a good chance to birdie the hole. The problem is if the ball hit a tree, I'm in deep trouble. I could get bogey or even worse. If I've tracked all my shots I had before like many pro golfers do, I can analyze my results under similar situation and make best decision based on my statistics. But apparently I don't have that data. So I would just follow my gut and go for the green today. By choice, maybe I should start tracking my shots so that I can make better decisions based on my data next time.